Reachability Analysis of Deep Neural Networks with Provable Guarantees
Authors: Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The technique has been implemented and evaluated on a range of DNNs, demonstrating its efficiency, scalability and ability to handle a broader class of networks than state-of-the-art verification approaches. |
| Researcher Affiliation | Academia | 1 University of Oxford, Oxford, UK 2 University of Liverpool, Liverpool, UK |
| Pseudocode | Yes | In i-th iteration, we do the following sequentially: Compute yi = arg infx [a,b] H(x; Yi 1) as follows. Let z = min Zi 1 and k be the index of the interval [yk 1, yk] where z is computed. Then we let yi = yk 1 + yk / 2 - (w(yk) - w(yk 1)) / (2K) and have that yi (yk 1, yk). Let Yi = Yi 1 {yi}, then reorder Yi in ascending order, and update w(Yi) = w(Yi 1) {w(yi)}. Calculate zi 1 = w(yi) + w(yk 1) - K(yi - yk 1) and zi = w(yk) + w(yi) and update Zi = (Zi 1 \ {z }) {zi 1, zi}. Calculate the new lower bound li = infx [a,b] H(x; Yi) by letting li = min Zi, and updating Li = Li 1 {li}. Calculate the new upper bound ui = miny Yi w(y) by letting ui = min{ui 1, w(yi)}. |
| Open Source Code | Yes | Available on https://github.com/trustAI/DeepGO. |
| Open Datasets | Yes | Seven convolutional neural networks, represented as DNN1,...,DNN-7, were trained on the MNIST dataset. |
| Dataset Splits | No | The paper mentions training on the MNIST dataset and reports "Testing accuracies" but does not specify the exact training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | Our software is implemented in Matlab 2018a, running on a notebook computer with i7-7700HQ CPU and 16GB RAM. |
| Software Dependencies | Yes | Our software is implemented in Matlab 2018a |
| Experiment Setup | Yes | Testing accuracies range from 95% to 99%, and ϵ = 0.05 is used in our experiments. |